Bruce M. Robinson, M.D., is a research fellow in adult nephrology at the University of Pennsylvania School of Medicine, and he is enrolled in the Master of Science in Clinical Epidemiology program at Penn's Center for Clinical Epidemiology and Biostatistics (CCEB). The career development component of the five year program proposed herein includes a preceptorship with Harold I. Feldman, M.D., Director of the Clinical Epidemiology Unit at the CCEB, and completion of the requirements for a Ph.D. degree in Clinical Epidemiology. Dr. Robinson's coursework will include advanced studies in causal inference and techniques of multivariate and longitudinal data analysis. This didactic program will extend Dr. Robinson's prior training, will be directly applicable to the timely completion of his research proposal, and will provide him with the necessary foundation to become an independent investigator in the epidemiology of renal disease. Dr. Robinson's research project, building on the infrastructure of the NIDDK's Chronic Renal Insufficiency Cohort (CRIC) Study, will be one of the first epidemiological studies of insulin resistance and the metabolic syndrome in chronic renal insufficiency (CRI). Affecting an estimated 20 million Americans, CRI carries a tremendous health burden due largely to a high risk of atherosclerotic cardiovascular disease (ASCVD). Insulin resistance is a risk factor for ASCVD in subjects with normal renal function and is highly prevalent in CRI. The primary goals of Dr. Robinson's project are to determine the relationships in CRI of insulin resistance to degree of renal insufficiency, to other metabolic syndrome features, to ASCVD events, and to CRI progression; and a secondary goal is to obtain further insight into insulin metabolism in CRI. A validated insulin resistance measure will be calculated from fasting insulin and glucose levels for all CRIC subjects. C-peptide, apolipoprotein B-100, and free fatty acid levels will be obtained in a nested CRIC sample. Using cross-sectional and longitudinal study designs, multivariable regression analyses will be used to address the stated goals and to characterize possible effect modification. By factor analysis, mechanistic structure underlying metabolic syndrome features in CRI will be inferred. By contributing to current understanding of the excess ASCVD risk in CRI, this project's findings can be applied toward identification of high risk CRI patients and will help in the development of therapies that target critical metabolic pathways.